text: from collections import deque
  
  import numpy as np
  import gym
  from gym import spaces
  import cv2  # pytype:disable=import-error
  cv2.ocl.setUseOpenCL(False)
  
  
  class NoopResetEnv(gym.Wrapper):
      def __init__(self, env, noop_max=30):
          """
          Sample initial states by taking random number of no-ops on reset.
          No-op is assumed to be action 0.
  
          :param env: (Gym Environment) the environment to wrap
          :param noop_max: (int) the maximum value of no-ops to run
          """
          gym.Wrapper.__init__(self, env)
          self.noop_max = noop_max
          self.override_num_noops = None
          self.noop_action = 0
          assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
  
      def reset(self, **kwargs):
          self.env.reset(**kwargs)
          if self.override_num_noops is not None:
              noops = self.override_num_noops
          else:
              noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)
          assert noops > 0
          obs = None
          for _ in range(noops):
              obs, _, done, _ = self.env.step(self.noop_action)
              if done:
                  obs = self.env.reset(**kwargs)
          return obs
  
      def step(self, action):
          return self.env.step(action)
  
  
  class FireResetEnv(gym.Wrapper):
      def __init__(self, env):
          """
          Take action on reset for environments that are fixed until firing.
  
          :param env: (Gym Environment) the environment to wrap
          """
          gym.Wrapper.__init__(self, env)
          assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
          assert len(env.unwrapped.get_action_meanings()) >= 3
  
      def reset(self, **kwargs):
          self.env.reset(**kwargs)
          obs, _, done, _ = self.env.step(1)
          if done:
              self.env.reset(**kwargs)
          obs, _, done, _ = self.env.step(2)
          if done:
              self.env.reset(**kwargs)
          return obs
  
      def step(self, action):
          return self.env.step(action)
  
  
  class EpisodicLifeEnv(gym.Wrapper):
      def __init__(self, env):
          """
          Make end-of-life == end-of-episode, but only reset on true game over.
          Done by DeepMind for the DQN and co. since it helps value estimation.
  
          :param env: (Gym Environment) the environment to wrap
          """
          gym.Wrapper.__init__(self, env)
          self.lives = 0
          self.was_real_done = True
  
      def step(self, action):
          obs, reward, done, info = self.env.step(action)
          self.was_real_done = done
          # check current lives, make loss of life terminal,
          # then update lives to handle bonus lives
          lives = self.env.unwrapped.ale.lives()
          if 0 < lives < self.lives:
              # for Qbert sometimes we stay in lives == 0 condtion for a few frames
              # so its important to keep lives > 0, so that we only reset once
              # the environment advertises done.
              done = True
          self.lives = lives
          return obs, reward, done, info
  
      def reset(self, **kwargs):
          """
          Calls the Gym environment reset, only when lives are exhausted.
          This way all states are still reachable even though lives are episodic,
          and the learner need not know about any of this behind-the-scenes.
  
          :param kwargs: Extra keywords passed to env.reset() call
          :return: ([int] or [float]) the first observation of the environment
          """
          if self.was_real_done:
              obs = self.env.reset(**kwargs)
          else:
              # no-op step to advance from terminal/lost life state
              obs, _, _, _ = self.env.step(0)
          self.lives = self.env.unwrapped.ale.lives()
          return obs
  
  
  class MaxAndSkipEnv(gym.Wrapper):
      def __init__(self, env, skip=4):
          """
          Return only every `skip`-th frame (frameskipping)
  
          :param env: (Gym Environment) the environment
          :param skip: (int) number of `skip`-th frame
          """
          gym.Wrapper.__init__(self, env)
          # most recent raw observations (for max pooling across time steps)
          self._obs_buffer = np.zeros((2,) + env.observation_space.shape, dtype=env.observation_space.dtype)
          self._skip = skip
  
      def step(self, action):
          """
          Step the environment with the given action
          Repeat action, sum reward, and max over last observations.
  
          :param action: ([int] or [float]) the action
          :return: ([int] or [float], [float], [bool], dict) observation, reward, done, information
          """
          total_reward = 0.0
          done = None
          for i in range(self._skip):
              obs, reward, done, info = self.env.step(action)
              if i == self._skip - 2:
                  self._obs_buffer[0] = obs
              if i == self._skip - 1:
                  self._obs_buffer[1] = obs
              total_reward += reward
              if done:
                  break
          # Note that the observation on the done=True frame
          # doesn't matter
          max_frame = self._obs_buffer.max(axis=0)
  
          return max_frame, total_reward, done, info
  
      def reset(self, **kwargs):
          return self.env.reset(**kwargs)
  
  
  class ClipRewardEnv(gym.RewardWrapper):
      def __init__(self, env):
          """
          clips the reward to {+1, 0, -1} by its sign.
  
          :param env: (Gym Environment) the environment
          """
          gym.RewardWrapper.__init__(self, env)
  
      def reward(self, reward):
          """
          Bin reward to {+1, 0, -1} by its sign.
  
          :param reward: (float)
          """
          return np.sign(reward)
  
  
  class WarpFrame(gym.ObservationWrapper):
      def __init__(self, env):
          """
          Warp frames to 84x84 as done in the Nature paper and later work.
  
          :param env: (Gym Environment) the environment
          """
          gym.ObservationWrapper.__init__(self, env)
          self.width = 84
          self.height = 84
          self.observation_space = spaces.Box(low=0, high=255, shape=(self.height, self.width, 1),
                                              dtype=env.observation_space.dtype)
  
      def observation(self, frame):
          """
          returns the current observation from a frame
  
          :param frame: ([int] or [float]) environment frame
          :return: ([int] or [float]) the observation
          """
          frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
          frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
          return frame[:, :, None]
  
  
  class FrameStack(gym.Wrapper):
      def __init__(self, env, n_frames):
          """Stack n_frames last frames.
  
          Returns lazy array, which is much more memory efficient.
  
          See Also
          --------
          myGym.stable_baselines_mygym.common.atari_wrappers.LazyFrames
  
          :param env: (Gym Environment) the environment
          :param n_frames: (int) the number of frames to stack
          """
          gym.Wrapper.__init__(self, env)
          self.n_frames = n_frames
          self.frames = deque([], maxlen=n_frames)
          shp = env.observation_space.shape
          self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * n_frames),
                                              dtype=env.observation_space.dtype)
  
      def reset(self, **kwargs):
          obs = self.env.reset(**kwargs)
          for _ in range(self.n_frames):
              self.frames.append(obs)
          return self._get_ob()
  
      def step(self, action):
          obs, reward, done, info = self.env.step(action)
          self.frames.append(obs)
          return self._get_ob(), reward, done, info
  
      def _get_ob(self):
          assert len(self.frames) == self.n_frames
          return LazyFrames(list(self.frames))
  
  
  class ScaledFloatFrame(gym.ObservationWrapper):
      def __init__(self, env):
          gym.ObservationWrapper.__init__(self, env)
          self.observation_space = spaces.Box(low=0, high=1.0, shape=env.observation_space.shape, dtype=np.float32)
  
      def observation(self, observation):
          # careful! This undoes the memory optimization, use
          # with smaller replay buffers only.
          return np.array(observation).astype(np.float32) / 255.0
  
  
  class LazyFrames(object):
      def __init__(self, frames):
          """
          This object ensures that common frames between the observations are only stored once.
          It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
          buffers.
  
          This object should only be converted to np.ndarray before being passed to the model.
  
          :param frames: ([int] or [float]) environment frames
          """
          self._frames = frames
          self._out = None
  
      def _force(self):
          if self._out is None:
              self._out = np.concatenate(self._frames, axis=2)
              self._frames = None
          return self._out
  
      def __array__(self, dtype=None):
          out = self._force()
          if dtype is not None:
              out = out.astype(dtype)
          return out
  
      def __len__(self):
          return len(self._force())
  
      def __getitem__(self, i):
          return self._force()[i]
  
  
  def make_atari(env_id):
      """
      Create a wrapped atari Environment
  
      :param env_id: (str) the environment ID
      :return: (Gym Environment) the wrapped atari environment
      """
      env = gym.make(env_id)
      assert 'NoFrameskip' in env.spec.id
      env = NoopResetEnv(env, noop_max=30)
      env = MaxAndSkipEnv(env, skip=4)
      return env
  
  
  def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):
      """
      Configure environment for DeepMind-style Atari.
  
      :param env: (Gym Environment) the atari environment
      :param episode_life: (bool) wrap the episode life wrapper
      :param clip_rewards: (bool) wrap the reward clipping wrapper
      :param frame_stack: (bool) wrap the frame stacking wrapper
      :param scale: (bool) wrap the scaling observation wrapper
      :return: (Gym Environment) the wrapped atari environment
      """
      if episode_life:
          env = EpisodicLifeEnv(env)
      if 'FIRE' in env.unwrapped.get_action_meanings():
          env = FireResetEnv(env)
      env = WarpFrame(env)
      if scale:
          env = ScaledFloatFrame(env)
      if clip_rewards:
          env = ClipRewardEnv(env)
      if frame_stack:
          env = FrameStack(env, 4)
      return env
  